Currently, the macrocells are deployed by operators. Since the deployment of femtocells can be in orders of magnitude more numerous than traditional cellular deployments and a network operator may not be able to control the femtocells directly. The femtocells are self-deployed by users rather than operators. Therefore, the femtocell base station's (BS) self-optimization deployment control software must have the characteristics of easy operation to make the BS with the least human action to satisfy the required performance, which are stated hereinafter.
The user just needs to plug-and-play and the BS of the femtocell can automatically configure the system parameters in the MU and interference indoor environments. In addition, the self-optimization control software deployed in an interference environment can self-optimization control the transmit power of the BS to save energy, reduce co-channel interference for the adjacent cell, and meet the requirement of service reliability. User input settings include service reliability, the cell edge throughput corresponding to the cell edge CQI and cell radius to match the size of the room coverage.
A previous study [1] has proposed a coverage adaptation approach for femtocell deployment in order to minimize the increase of core network mobility signaling. The information on mobility events of passing and indoor users are used to optimize the femtocell coverage. An approach based on genetic algorithm was presented in [2] to automatically optimize the coverage of a group of femtocells in an enterprise environment. The algorithm is able to dynamically update the pilot powers of the femtocells as per the time varying global traffic distribution and interference levels. The algorithm in a decentralized femtocell deployment has not been considered. [3] has proposed an adaptive neural fuzzy inference system (ANFIS)-assisted power control scheme for a multi-rate multimedia direct-sequence code-division multiple-access (DS-CDMA) system to precisely predict the channel variations and thus compensate for the effect of signal fading in advance. The author in the above study also provides a procedure for determining the transmission rate based upon the output of the signal-to-interference and noise ratio (SINR) increment of the ANFIS power control mechanisms at the sample period. The fuzzy membership functions of ANFIS power control mechanisms use seven Gaussian functions, so that there are 49 fuzzy inference rules. The ANFIS power control mechanisms use two input variables, including SINR error e(n) and SINR error change Δe(n), to track the set point of target SINR. In the present technique, the target SINR value is set to a fix value of 1.5 dB, let the power control process is not flexible enough. The input parameters of ANFIS power control mechanism totally depend on SINR control efficiency. The power cannot be controlled by channel environment. The technology has not considered the performance of multi-user (MU) service reliability (SR).    [1] Holger Claussen et al., Self-optimization of Coverage for Femtocell Deployments, Bell Labs Technical Journal—Core and Wireless Networks, Volume 14 Issue 2, August 2009, Pages 155-183.    [2] Lina S. Mohjazi et al., Self-Optimization of Pilot Power in Enterprise Femtocells Using Multi objective Heuristic, Journal of Computer Networks and Communications, Volume 2012.    [3] C. H. Jiang, J. K. Lian, R. M. Weng, C. H. Hsu, “Multi-rate DS-CDMA with ANFIS-assisted power control for wireless multi-media communications,” International Journal of Innovative Computing, Information and Control, vol. 6, no. 8, pp. 3641-3655, August 2010.